COVID-19 short-term forecasts Deaths 2020-09-12 Latin American Countries


General information

  • Forecasts produced by Jennie Castle, Jurgen Doornik, and David Hendry, researchers at the University of Oxford. These are our forecasts, and should not be considered official forecasts from, or endorsed by, any of: University of Oxford, Oxford Martin School, Nuffield College, or Magdalen College.
  • These forecasts are short term time-series extrapolations of the data. They are not based on epidemiological modelling or simulations. All forecasts are uncertain: their success can only be determined afterwards. Many mitigation strategies are in place, which, if successful, invalidate our forecasts. An explanation of our methods is provided below.
  • A list of notes is below. The most recent note:
    [2020-07-01] Modified the short-term model to allow for (slowly changing) seasonality. Many countries show clear seasonality after the initial period, likely caused by institutional factors regarding data collection. This seasonality was also getting in the way of peak detection. As a consequence estimates of the peak date may have changed for countries with strong seasonality.
    Added forecasts of cumulative confirmed cases for lower tier local authorities of England. The data is available from 2020-07-02 including all tests (pillar one and two). Only authorities with more than 5 cases in the previous week are included.

Peak increase in estimated trend of Deaths in Latin America 2020-09-12

ArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHaitiHondurasMexicoPanamaParaguayPeruVenezuela
Peak date --09-0707-2207-17 --09-0704-1209-0708-1006-0507-1007-3006-2407-23 --07-23 --
Peak daily increment 1555 1062 785 19 22 3844 11 43 6 35 683 28 2947
Days from 100 to peak 122 116 93 43 5 159 49 5 12 83 79 98 107
Days from peak/2 to peak 78 107 63 109 17 120 119 29 83 114 72 116 78
Last total 11263 7297 131210 11895 22734 590 1953 10864 782 2949 219 2065 70604 2155 514 30470 477
Last daily increment 115 47 814 45 216 7 12 28 5 20 3 7 421 15 18 126 9
Last week 1404 1899 4560 303 1322 112 108 4140 23 97 5 58 3046 69 79 783 49
Days since peak 5 52 57 5 153 5 33 99 64 44 80 51 51

Deaths count forecast Latin America (bold red line in graphs) 2020-09-13 to 2020-09-19

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-12 11263 7297 131210 11895 22734 590 1953 10864 782 2949 2065 70604 2155 514 30470 477
2020-09-13 11350 7397 132100 11960 22970 608 1972 11050 790 2965 2081 71060 2167 606 30600 487
2020-09-14 11620 7476 132400 12020 23190 632 1992 11190 797 2980 2098 71260 2179 635 30730 497
2020-09-15 11880 7558 133100 12050 23410 647 2010 11340 804 2996 2115 71950 2191 670 30860 508
2020-09-16 12120 7629 134100 12070 23620 659 2028 11460 811 3011 2132 72490 2202 690 30990 518
2020-09-17 12350 7700 135000 12150 23840 679 2047 11580 819 3026 2149 73020 2214 712 31120 529
2020-09-18 12590 7771 135700 12230 24060 692 2066 11700 826 3041 2167 73510 2225 737 31250 541
2020-09-19 12710 7842 136500 12280 24270 704 2084 11820 833 3057 2184 73930 2237 737 31380 552

Deaths count average forecast Latin America (bold black line in graphs) 2020-09-13 to 2020-09-19

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-12 11263 7297 131210 11895 22734 590 1953 10864 782 2949 2065 70604 2155 514 30470 477
2020-09-13 11400 7364 131600 11940 23010 601 1970 10970 786 2952 2077 70820 2162 536 30540 486
2020-09-14 11630 7513 132100 11990 23260 619 1990 11110 792 2962 2093 71030 2173 553 30680 495
2020-09-15 11870 7616 132700 12040 23450 635 2011 11250 797 2978 2111 71710 2184 571 30810 505
2020-09-16 12100 7720 133700 12080 23760 649 2031 11390 802 2990 2128 72250 2195 588 30960 515
2020-09-17 12320 7820 134500 12150 24040 667 2052 11530 808 3007 2146 72800 2206 605 31110 525
2020-09-18 12540 7922 135400 12220 24320 685 2073 11690 814 3023 2165 73290 2218 623 31290 535
2020-09-19 12720 8061 136100 12280 24590 702 2094 11860 820 3041 2183 73800 2229 639 31440 546

Deaths count scenario forecast (bold purple line in graphs) 2020-09-13 to 2020-09-21

DateArgentinaBoliviaBrazilChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHondurasMexicoPanamaParaguayPeruVenezuela
2020-09-12 11263 7297 131210 11895 22734 590 1953 10864 782 2949 2065 70604 2155 514 30470 477
2020-09-13 11470 7776 131900 11940 22940 610 1972 11890 786 2960 2086 71080 2165 527 30580 484
2020-09-14 11680 8197 132500 11980 23130 629 1992 12570 790 2971 2094 71540 2174 541 30670 493
2020-09-15 11910 8211 133200 12020 23320 645 2012 13260 793 2980 2109 71990 2182 550 30760 501
2020-09-16 12150 8335 133800 12060 23520 662 2033 13830 797 2992 2117 72440 2190 561 30850 510
2020-09-17 12390 8570 134400 12090 23690 679 2052 14300 800 3002 2124 72850 2198 579 30940 519
2020-09-18 12650 8748 134900 12120 23880 695 2074 14780 803 3010 2135 73250 2205 594 31010 528
2020-09-19 12890 8969 135400 12150 24030 711 2092 15420 805 3014 2139 73670 2212 613 31080 537
2020-09-20 13140 9163 135900 12170 24150 726 2111 15870 807 3019 2143 74060 2220 623 31150 547
2020-09-21 13390 9361 136500 12200 24300 739 2129 16380 809 3024 2149 74410 2227 633 31220 554

Further information

  • We believe these forecasts fill a useful gap in the short run. They give an indication of what is likely to happen in the next few days, removing some aspect of surprise. Moreover, a noticeable drop in comparison to the extrapolations could be an indication that the implemented policies are having some impact. It is difficult to understand exponential growth. We hope that these forecasts may help to convince viewers to adhere to the policies implemented by their respective governments, and keep all arguments factual and measured.
  • We use the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering. This is updated daily, but we tend to update our forecasts only every other day.
    US state data as of 2020-03-28 is courtesy of the New York Times.
  • We can only provide forecasts of what is measured. If confirmed cases are an underestimate of actual cases, then our forecasts will also be underestimates. No other epidemiological data is used. Data definition and collection differs between countries and may change over time.
  • We will update the methodology as we learn what is happening in the next few days or weeks. Once the number of cases levels off, there is no need to provide these forecasts anymore.
  • Countries where the counts are very low or stable have been omitted.
  • The graphs have dates on the horizontal axis (yyyy-mm-dd) and cumulative counts on the vertical axis. They show
    1. bold dark grey line (with circles): observed counts (Johns Hopkins CSSE);
    2. many light grey lines (with open circles): forecasts using different model settings and starting up to four periods back;
    3. red line (with open circles): single forecasts path using default model settings;
    4. black line (with crosses): average of all forecasts, recentered on the last observation;
    5. thin green lines: some indication of uncertainty around the red forecasts, but we do not know how reliable that is.
    Both the red line forecasts and the black lines are also given in the tables above. These forecasts differ, we are currently inclined to use the average forecasts.
  • The forecasts are constructed as follows:
    1. An overall `trend' is extracted by taking a window of the data at a time. In each window we draw `straight lines' which are selected using an automatic econometric procedure (`machine learning'). All straight lines are collected and averaged, giving the trend.
    2. Forecasts are made using the estimated trend, but we note that this must be done carefully, because simply extrapolating the flexible insample trend would lead to wildly fluctuating forecast. We use the `Cardt' method, which has been found to work well in other settings.
    3. Residuals from the trend are also forecast, and combined with trend forecasts into an overall forecast.
  • Scenario forecasts are constructed very differently: smooth versions of the Chinese experience are matched at different lag lengths with the path of each country. This probably works best from the peak, or the slowdown just before (but we include it for the UK nonetheless).
  • The forecast evaluation shows past forecasts, together with the outcomes (in the grey line with circles).
  • EU-BS is Estonia, Latvia, and Lithuania together.
  • This paper describes the methodology and gives further references. Also available as Nuffield Economics Discussion Paper 2020-W06. Still preliminary is the documentation of the medium term forecasts.

Recent changes and notes

[2020-07-01] Modified the short-term model to allow for (slowly changing) seasonality. Many countries show clear seasonality after the initial period, likely caused by institutional factors regarding data collection. This seasonality was also getting in the way of peak detection. As a consequence estimates of the peak date may have changed for countries with strong seasonality.
Added forecasts of cumulative confirmed cases for lower tier local authorities of England. The data is available from 2020-07-02 including all tests (pillar one and two). Only authorities with more than 5 cases in the previous week are included.
[2020-06-29] Tables in April included the world, but not the world as we know it (double counting China and the US). So removed the world from those old tables.
Why short-term forecasts can be better than models for predicting how pandemics evolve just appeared at The Conversation.
Thursday 2 July webinar at the FGV EESP - São Paolo School of Economics. This starts at 16:00 UK time (UTC+01:00) and streamed here.
[2020-06-24] Research presentation on short-term COVID-19 forecasting on 26 June (14:00 UK time) at the Quarterly Forecasting Forum of the IIF UK Chapter.
[2020-06-06] Removed Brazil from yesterday's forecasts (only; last observation 2020-06-05).
[2020-06-04] Data issues with confirmed cases for France.
Added an appendix to the short term paper with further forecast comparisons for European and Latin American countries.
Both Sweden and Iran have lost their peak in confirmed cases. For Sweden the previous peak was on 24 April (daily peak of 656 cases), for Iran it was on 31 March (peak of 3116). For Iran this looks like a second wave, with increasing daily counts for the last four weeks. For Sweden this is a sudden jump in confirmed cases in the last two days, compared to a fairly steady weekly pattern over the previous six weeks.
[2020-05-20] Problem with UK confirmed cases: negative daily count. This makes the forecasts temporarily unreliable.
Updated the second paper.
[2020-05-18] Minor fixes to the improved version of scenario forecasting, backported to 2020-05-13.
[2020-05-13] We now omit countries with fewer than 200 confirmed cases in the last week (25 for deaths).
The short-term paper has some small updates, including further comparisons with other models.
Data for Ecuador are not reliable enough for forecasting.
Switched to an improved version of scenario forecasting.
[2020-05-06] The New York Times is in the process of redefining its US state data. Unfortunately, at the moment only the last observation has changed (e.g New York deaths jumped from 19645 on 2020-05-05 to 25956 a day later). This means the data is currently useless; however it does bring it close to the Johns Hopkins/CSSE count (25626 on 2020-05-06). The aggregate US count is based on JH/CSSE so unaffected. We now use Johns Hopkins/CSSE US state data, including all states with sufficient counts. So the new forecasts cannot be compared to those previously.
A minor change is that we show the graph without scenario forecast if no peak has been detected yet.
[2020-04-29] See our blog entry at the International Institute of Forecasters.
US history of death counts revised in Johns Hopkins/CSSE data.
UK death counts have been revised to include the deaths in care homes. In the Johns Hopkins/CSSE data set, which we use, the entire history has been revised. So forecasts made up to 2020-04-29 cannot be compared to later outcomes. In the ECDC data set only the last observation has changed, causing a jump in the series.
[2020-04-27] Our short-term COVID-19 forecasting paper is now available as Nuffield Economics Discussion Paper 2020-W06.
A small adjustment has been made to the scenario forecast methodology, and will be documented shortly.
[2020-04-24] A summary of our work on short-term COVID-19 forecasting appeared as a voxeu.
[2020-04-17] Bird and Nielsen look into nowcasting death counts in England.
[2020-04-16] Added scenario forecasts to all graphs now. This would now be the preferred forecast for most.
This is the first time with a peak in confirmed UK cases (also for deaths, but this is uncertain because it is at the same date).
[2020-04-10] Updated documentation with better description of short-term estimates and peak determination.
[2020-04-09] Added table with estimated peak dates (if happened) and dates to and since the peak. Note that this can be a local peak, and subsequent re-acceleration (or data revisions) can result in a new peak later.
[2020-04-08] Minor correction to peak estimates. Added table with scenario forecasts.
[2020-04-06] Added a post hoc estimate of the peak number of cases. This needs at least three confirmed observations (four for deaths) after the event. It is based on the averaged smooth trend, and can change later or be a local peak. It is marked with a vertical line with the date label, or a date with left arrow in the bottom left corner of the graph. This is backported to 2020-04-04.
[2020-04-02] Now including more US States, based on New York Times data.
[2020-03-31] Scenario forecasts, based on what happened in China earlier this year, are presented for several countries (line marked with x). Created more plausible 90% confidence bands (dotted line in same colour).
[2020-03-26] Scenario forecasts that are based on what happened in China earlier this year, only for Italy.
[2020-03-24] Our forecasts are starting to overestimate in some cases. This was always expected to happen when the increase starts to slow down. Scenario forecasts that are based on what happened in China earlier this year, but only for Italy and Spain sofar.

Initial visual evaluation of forecasts of Deaths